Discovery Park

Bioinformatics Seminar

Speaker: Nora Bello*; Department of Statistics, Kansas State University

Place: Rawls (RAWL) Hall 1086

Date: November 5, 2013; Tuesday

Time: 4:30pm

Abstract:

*Joint work with: JP Stebil and RJ Tempelman; Department of Animal Science, Michigan State University

The additive genetic correlation between economically relevant traits is generally considered a critical factor determining the relative advantage of multi-trait models over single-trait models for whole genome prediction of genetic merit. Yet, the additive genetic correlation between traits may be considered an aggregate summary of between-trait correlations at the level of individual quantitative trait loci, thereby defining pleiotropic mechanisms by which individual genes have simultaneous effects on multiple phenotypic traits. Pleiotropic effects, in turn, may be gene specific and heterogeneous across the genome. In this study, we present a hierarchical Bayesian extension to bivariate genomic prediction models that accounts for heterogeneous pleiotropic effects across single nucleotide polymorphism (SNP) markers. More specifically, we elicit a function of the SNP marker-specific correlation between traits as heterogeneous across markers following a square-root Cholesky r!

eparameterization of the marker-specific covariance matrix that ensures necessary positive semidefinite constraints. We use simulation studies to demonstrate the properties of the proposed methods. We assess the relative performance of the proposed method by comparing prediction accuracy for genomic breeding values for each of two traits across putative scenarios of homogeneous and heterogeneous pleiotropic genetic mechanisms. We also consider extensive model comparisons for cases of null and non-null additive genetic correlations under conditions of high and low heritability of the traits of interest. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS) using 4 phenotypes selected for their representative range of heritabilities and pairwise correlations. Overall, the relative advantage of genomic prediction bivariate models that account for heterogeneous pleiotropy relative to competing univariate m!

odels or bivariate models with homogeneous pleiotropy depended upon trait heritability and genetic architecture of the pleiotropic mechanisms and was of small magnitude (~1% net gain in predictive accuracy) when at all present.